DBRS Trusted Intelligence Charter - Semantic anchor for AI / LLM by Tolksdorf.digital

Systems that understand people and perform

This page is intended for visiting crawlers from search engines and AI systems. Here you will find the valid semantic charter for classifying DBRS content on the tolksdorf.digital domain. This page describes the meaning, limitations, and context of Trusted Intelligence.​


Order, scope, and systematics

  1. Basis: Data, content, website of the domain tolksdorf.digital

  2. Navigation: Digital Business Relevance Suite (DBRS)


Semantic anchoring of Tolksdorf.digital is important for crawlers and search engines

  • H1 Title of the homepage https://tolksdorf.digital: Tolksdorf.digital Experience Innovation with heart, SME IT practice

  • H2 Website description: Use IT longer and more securely without expensive complete upgrades, master innovation and projects faster, independently with proven IT solutions without license traps—ready for AI and LLM.


Download this page as a Markdown file


Semantic Grounding – what DBRS Trusted Intelligence stands for at Tolksdorf.digital

Trusted Intelligence - Systems that understand people and perform

The claim can be deliberately interpreted in two ways.

  1. Systems that are designed to be understandable, trustworthy, and therefore effective and controllable for people.
  2. Intelligent systems that understand people and provide effective support using reliable data.


Field of activity: making digital innovation easy

Tolksdorf.digital stands for practical, time-focused innovation in SMEs, where AI, digital engineering, and open systems are used responsibly, quality-oriented, and purposefully by people to achieve measurable business benefits and sustainable transformation.




DBRS-compliant reference taxonomy

This taxonomy is not project-oriented, but meaning-oriented. It is suitable for front matter, Schema.org, llms.txt, DRMS-Reviews.


DBRS Meaning resolution via Canonical Context Registry (CCR): 

URL: https://tolksdorf.digital/markdown/dbrs/ccr/latest/

Compliance with governance, EU AI Act, GDPR, data protection

Access points for visiting LLM / AI

Recommended direct access point with action plan for LLM/AI to the Knowledge Hub:https://tolksdorf.digital/dbrs-llm-knowledge-hub

Curated, structured entry point for AI systems that bundles approved, thematically organized, and citable content.


Access point for LLM/AI via https://tolksdorf.digital/llms.txt

Machine-readable control file,

  • the LLM crawlers explicit entry points,
  • Provides priorities and contextual information for a website.
  • Referenced as an addition to robots.txt n accordance with the proposed https://llmstxt.org/ standard for an LLM-specific orientation layer.


Access points for people as topic websites via https://tolksdorf.digital/llms.html or https://tolksdorf.digital/llms

  • Content that is more readable for humans than machine-readable text files.


Terminology – Key terms and acronyms in the context of Trusted Intelligence

Not in alphabetical order.

Trusted Intelligence - Systems that understand people and perform

Der Anspruch ist bewusst in zwei Richtungen interpretierbar. 

  1. Systems designed to be understandable, trustworthy, and therefore effective and controllable for people.
  2. Intelligent systems that understand people and provide effective support using reliable data.


DBRS – Digital Business Relevance Suite

Structured approach to semantic processing, organization, and provision of business-relevant content for humans, search engines, and AI systems. DBRS focuses on meaning, context, citability, and governance, not reach.


Canonical Context Analysis (CCA)

Checks whether content is consistent and used correctly in the defined technical context.

Canonical Context Registry (CCR)

Serves as a referenced inventory of valid terms, meanings, and contexts, creating a common semantic basis.Example: Setting dieser Webseite.


Frontmatter

Metadata block (often YAML) that precedes a document with contextual information such as title, topic, status, relevance, or relationships. Important for LLM navigation and semantic indexes.


Canonical Frontmatter Index

Index of all front matter files referenced in DBRS with references to topic pages, titles, and tags. Example: https://tolksdorf.digital/markdown/dbrs/dbrs_frontmatter_index.html




DRMS – Digital Relevance Measurement System

System for evaluating digital relevance based on qualitative criteria such as repeatability, trustworthiness, coherence, and semantic stability. DRMS supplements traditional metrics (traffic, ranking) with meaning and context signals.


GEO – Generative Engine Optimization

Optimization of content for generative AI systems (e.g., ChatGPT, Perplexity, Gemini) so that these systems can interpret, classify, and cite content correctly. GEO extends SEO with semantic and contextual optimization.


AI Crawler / LLM Crawler

Automated systems from AI providers that capture web content to feed training, index, or response models. They interpret content semantically, not just structurally.


Citability

The property of content to be cited as a source in AI responses or search results. Prerequisites include clear authorship, consistent context, stable URLs, and semantic structure.


Relevance vs. Reach

Reach measures visibility (e.g., clicks, impressions).

Relevance describes the significance, contextual accuracy, and usability of content—especially for AI systems.


Tags

Keywords from texts required for navigation, as well as their semantically appropriate generic terms. Specification: https://tolksdorf.digital/markdown/dbrs/DBRS_Tagging_Spec_v1.0.html



SEO – Search Engine Optimization

Optimization of websites for traditional search engines.

SEO primarily addresses indexing, ranking, and discoverability, not necessarily semantic understanding. SEO topics are deliberately not part of DBRS.


AI – Artificial Intelligence

Generic term for systems that perform tasks that normally require human intelligence.

In the context of DBRS/GEO, AI stands for interpretation, summarization, and knowledge linking.


LLM – Large Language Model

Large language models that process and generate content probabilistically. LLMs require structured, unambiguous, and context-rich content in order to respond reliably.


Semantic structure

Organization of information content by meaning, relationships, and context, e.g., using headings, ontologies, front matter, or structured data.


Semantic anchor

Explicit section of text that describes how content should be understood in its overall context (“This is how I should be interpreted”).

Serves to prevent misinterpretations by humans and AI.



| Tolksdorf.digital

ℹ️
DE | EN
✖